Empirical methods based on band ratios to infer chlorophyll-a concentration by satellite do not perform well over the optically complex waters of the St. Lawrence Estuary and Gulf. Using a dataset of 93 match-ups, we explore an alternative method relying on empirical orthogonal functions (EOF) to develop an algorithm that relates the satellite-derived remote sensing reflectances to in situ chlorophyll-a concentration for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Results show that an accuracy of 41% at retrieving chlorophyll-a concentration can be reached using the EOF method compared to 140% for the widely-used Ocean Chlorophyll 4 (OC4v4) empirical algorithm, 53% for the Garver-Siegel-Maritorena (GSM01) and 54% for the Generalized Inherent Optical Property (GIOP) semi-analytical algorithms. This result is possible because the EOF approach is able to extract region-specific radiometric features from the satellite remote sensing reflectances that are related to absorption properties of optical components (water, coloured dissolved organic matter and chlorophyll-a) using the visible SeaWiFS channels. The method could easily be used with other ocean-colour satellite sensors (e.g., MODIS, MERIS, VIIRS, OLCI) to extend the time series for the St. Lawrence Estuary and Gulf waters.

The Arctic atmosphere–surface system transmits visible light from the Sun to the ocean, determining the annual cycle of light available to microalgae. This light is referred to as photosynthetically available radiation (PAR). A known consequence of Arctic warming is the change at the atmosphere–ocean interface (longer ice-free season, younger ice), implying an increase in the percentage of PAR being transferred to the water. However, much less is known about the recent changes in how much PAR is being transferred by the overlaying atmosphere. We studied the transfer of PAR through the atmosphere between May 21 and July 23 at a pan-Arctic scale for the period ranging from 2000 to 2016. By combining a large data set of atmospheric and surface conditions into a radiative transfer model, we computed the percentage of PAR transferred to the surface. We found that typical Arctic atmospheres convey between 60% and 70% of the incident PAR received from the Sun, meaning the Arctic atmosphere typically transmits more light than most sea ice surfaces, with the exception of mature melt ponds. We also found that the transfer of PAR through the atmosphere decreased at a rate of 2.3% per decade over the studied period, due to the increase in cloudiness and the weaker radiative interaction between the atmosphere and the surface. Further investigation is required to address how, in the warmer Arctic climate, this negative trend would compensate for the increased surface transmittance and its consequences on marine productivity.

Deriving sea ice albedo from spaceborne platforms is of interest to model the propagation of the photosynthetically available radiation (PAR) through Arctic sea ice. We show here that use of the Moderate Resolution Imaging Spectroradiometer (MODIS) operational surface reflectance satellite product to derive albedo in the PAR spectral range is possible. To retrieve PAR albedo from the remote sensing surface reflectance, we trained a predictive model based on a principal component analysis with in situ and simulated data. The predictive model can be applied to first-year sea ice surfaces such as dry snow, melting snow, bare ice and melt ponds. Based on in situ measurements and the prescribed atmospheric correction uncertainty, the estimated PAR albedo had a mean absolute error of 0.057, a root mean square error of 0.074 and an R² value of 0.91. As a demonstration, we retrieved PAR albedo on a 9-km² area over late spring and early summer 2015 and 2016 at a coastal location in Baffin Bay, Canada. On-site measurements of PAR albedo, melt pond fraction and types of precipitation were used to examine the estimated PAR albedo time series. The results show a dynamic and realistic PAR albedo time series, although clouds remained the major obstacle to the method. This easy-to-implement model may be used for the partitioning of PAR in the Arctic Ocean and ultimately to better understand the dynamics of marine primary producers.

Brightness temperature is operationally used to retrieve sea surface salinity (TB-SSS) over the global ocean, but is contaminated by land and sea ice in close proximity. Ocean color can be used to retrieve SSS (OC-SSS) via the relation between color and salinity, but this relation is only valid over the coastal ocean with terrestrial influence. Important ecological areas exist where both spectral domains can provide SSS estimates. Here we compare these estimates over the St. Lawrence Estuary and Gulf in Eastern Canada, where a large collection of near-surface in situ salinity measurements is available. While TB-SSS faces a significant limitation in undersampling spatial variability, OC-SSS is predominantly hindered by cloud cover. Offshore, TB-SSS data are considerably more abundant than OC-SSS data, the latter of which are available only about 30% as often as the former. However, OC-SSS estimates extend into more nearshore areas, such as the St. Lawrence Estuary. Additionally, OC-SSS estimates are more accurate, with a root mean square difference of 0.46 g kg ⁻¹ compared to 0.79 g kg ⁻¹ for TB-SSS. We employed each of these satellite-derived SSS products to compare the pronounced freshwater pulse of 2017 and post-tropical storm Dorian of fall 2019, finding that short-lived events were better captured by the OC-SSS product. In contrast, the TB-SSS product offered more extensive temporal coverage but smoothed out such events. Our analyses underscore the need for higher-resolution satellite salinity-sensors in coastal studies. In the meantime, ocean color data resolves submesoscale features and can help enhance our understanding of these dynamic environments.